Solute Carrier (SLC) superfamily members are membrane transporter proteins that control the uptake and efflux of solutes, including essential cellular compounds, environmental toxins and therapeutic drugs across biological membranes. The broad aim of this proposal is to contribute to our understanding of the genetic basis of variation among individuals'response to drugs (pharmacogenetics), by providing comprehensive integrated experimental and computational description of substrate specificity of this key membrane transporter superfamily. The first goal of this proposal is to identify new SLC superfamily members in the human genome and to elucidate the relationships between SLC families;this will be achieved by mapping sequence, structural and chemical features that distinguish one family from another. The classification into groups of related proteins will be useful for inferring similarities in structural and functional features (e.g., fold, ligand binding site, and molecular mechanism) of uncharacterized proteins based on their characterized aligned homologs. The classification scheme is likely to inform modeling of the SLC transporter structures - a prerequisite for describing their substrate specificities. The second goal is to describe sequence and structure determinants of substrate specificity within selected SLC families. This goal will be accomplished by combining computational methodologies such as sequence analysis, structure comparisons, comparative modeling, ligand docking, and virtual screening, with experimental efforts. The experimental work will be conducted in collaboration with other labs;it will include functional characterization using various assays including ligand uptake and kinetic measurements. The third goal is to utilize the specificity determinants information of the SLC families to develop predictive models for the effects of genetic variation on transporter function, which will be experimentally tested on clinically important substrates. Ultimately, the results obtained in this study will contribute to our understanding of why individuals respond differently to the same drug. In particular, correlation of the study results with clinically observed disease states and variation among individuals in response to drugs can be a significant step toward personalized medicine greatly benefitting public health.